Getting started

The xdatasets library enables users to effortlessly access a vast collection of earth observation datasets that are compatible with xarray formats.

The library adopts an opinionated approach to data querying and caters to the specific needs of certain user groups, such as hydrologists, climate scientists, and engineers. One of the functionalities of xdatasets is the ability to extract data at a specific location or within a designated region, such as a watershed or municipality, while also enabling spatial and temporal operations.

To use xdatasets, users must employ a query. A straightforward query to extract the variables t2m (2m temperature) and tp (Total precipitation) from the era5_reanalysis_single_levels dataset at two geographical positions (Montreal and Toronto) could be as follows:

query = {
    "variables": {"era5_reanalysis_single_levels": ["t2m", "tp"]},
    "space": {
        "clip": "point", # bbox, point or polygon
        "geometry": {'Montreal' : (45.508888, -73.561668),
                     'Toronto' : (43.651070, -79.347015)
                    }
    }
}

An example of a more complex query would look like this. This query calls the same variables as above. However, instead of specifying geographical positions, GeoPandas.DataFrame is used to provide features (such as shapefiles or geojson) for extracting data within each of them. Each polygon is identified using the unique identifier Station, and a spatial average is computed within each one (aggregation: True). The dataset, initially at an hourly time step, is converted into a daily time step while applying one or more temporal aggregations for each variable as prescribed in the query. The xdatasets function ultimately returns the dataset for the specified date range and time zone.

query = {
    "variables": {"era5_reanalysis_single_levels": ["t2m", "tp"]},
    "space": {
        "clip": "polygon", # bbox, point or polygon
        "aggregation": True,
        "geometry": gdf,
        "unique_id": "Station"
    },
    "time": {
        "timestep": "D",
        "aggregation": {"tp": np.nansum,
                        "t2m": [np.nanmax, np.nanmin]},

        "start": '1959-01-01',
        "end": '1961-05-31',
        "timezone": 'America/Montreal',
    },
}

Note

Don’t worry! Below, you’ll find additional examples that will assist in understanding each parameter in the query, as well as the possible combinations.

Query climate datasets

In order to utilize xdatasets, you must import at least xdatasets, pandas, geopandas, and numpy. Additionally, we import pathlib to interact with files.

%load_ext autoreload
%autoreload 2

import xdatasets as xd
import geopandas as gpd
import pandas as pd
import numpy as np

from pathlib import Path

Let’s first access certain polygon features, which can be in the form of shapefiles, geojson, or any other format compatible with geopandas. In this example, we are using JSON (geojson) files.

bucket = Path('https://s3.us-east-2.wasabisys.com/watersheds-polygons/MELCC/json')

paths = [bucket.joinpath('023003/023003.json'),
         bucket.joinpath('031101/031101.json'),
         bucket.joinpath('040111/040111.json')]

Subsequently, all of the files can be opened and consolidated into a single geopandas.GeoDataFrame object.

gdf = pd.concat([gpd.read_file(path) for path in paths]).reset_index(drop=True)
gdf

	Station	Superficie	geometry
0	023003	208.4591919813271	POLYGON ((-70.82601 46.81658, -70.82728 46.815...
1	031101	111.7131058782722	POLYGON ((-73.98519 45.21072, -73.98795 45.209...
2	040111	433.440893903503	POLYGON ((-74.06645 46.02253, -74.06647 46.022...

Let’s examine the geographic locations of the polygon features.

gdf.hvplot(geo=True,
           tiles='ESRI',
           color='Station',
           alpha=0.8,
           width=750,
           height=450,
           legend='top',
           hover_cols=['Station','Superficie'])

%%time
query = {
    "variables": {"era5_reanalysis_single_levels": ["t2m", "tp"]},
    "space": {
        "clip": "polygon", # bbox, point or polygon
        "aggregation": False,
        "geometry": gdf,
        "unique_id": "Station"
    },
    "time": {
        "start": '1979-01-01',
        "end": '2020-12-31',
    },
}

xds = xd.Dataset(**query)

3it [00:06,  2.13s/it]

CPU times: user 11.6 s, sys: 748 ms, total: 12.4 s
Wall time: 18.2 s

xds.data

<xarray.Dataset>
Dimensions:     (latitude: 6, longitude: 5, time: 368184, Station: 3)
Coordinates:
  * latitude    (latitude) float32 45.0 45.25 46.0 46.25 46.5 46.75
  * longitude   (longitude) float32 -74.5 -74.25 -74.0 -71.0 -70.75
  * time        (time) datetime64[ns] 1979-01-01 ... 2020-12-31T23:00:00
  * Station     (Station) object '023003' '031101' '040111'
    Superficie  (Station) object '208.4591919813271' ... '433.440893903503'
Data variables:
    t2m         (Station, time, latitude, longitude) float32 nan nan ... nan nan
    tp          (Station, time, latitude, longitude) float32 nan nan ... nan nan
    weights     (Station, latitude, longitude) float64 nan nan nan ... nan nan
Attributes:
    institution:  ECMWF
    source:       Reanalysis
    title:        ERA5 forecasts

xarray.Dataset

• Dimensions:
  • latitude: 6
  • longitude: 5
  • time: 368184
  • Station: 3
• Coordinates: (5)
  • latitude
    (latitude)
    float32
    45.0 45.25 46.0 46.25 46.5 46.75

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([45.  , 45.25, 46.  , 46.25, 46.5 , 46.75], dtype=float32)

  • longitude
    (longitude)
    float32
    -74.5 -74.25 -74.0 -71.0 -70.75

    array([-74.5 , -74.25, -74.  , -71.  , -70.75], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1979-01-01 ... 2020-12-31T23:00:00

    standard_name :

    time

    array(['1979-01-01T00:00:00.000000000', '1979-01-01T01:00:00.000000000',
           '1979-01-01T02:00:00.000000000', ..., '2020-12-31T21:00:00.000000000',
           '2020-12-31T22:00:00.000000000', '2020-12-31T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • Station
    (Station)
    object
    '023003' '031101' '040111'

    array(['023003', '031101', '040111'], dtype=object)

  • Superficie
    (Station)
    object
    '208.4591919813271' ... '433.440...

    array(['208.4591919813271', '111.7131058782722', '433.440893903503'],
          dtype=object)

• Data variables: (3)
  • t2m
    (Station, time, latitude, longitude)
    float32
    nan nan nan nan ... nan nan nan nan

    long_name :

    2 metre temperature

    nameCDM :

    2_metre_temperature_surface

    nameECMWF :

    2 metre temperature

    product_type :

    analysis

    shortNameECMWF :

    2t

    standard_name :

    air_temperature

    units :

    K

    array([[[[     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan, 270.8125, 270.3125],
             [     nan,      nan,      nan, 268.8125, 268.5625]],
    
            [[     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan, 270.9375, 270.5   ],
             [     nan,      nan,      nan, 269.0625, 268.9375]],
    
            [[     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan, 271.25  , 270.75  ],
             [     nan,      nan,      nan, 269.3125, 269.25  ]],
    ...
             [     nan,      nan,      nan,      nan,      nan],
             [268.5   , 268.3125, 269.25  ,      nan,      nan],
             [267.1875, 266.75  ,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan]],
    
            [[     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [266.6875, 266.5625, 267.5   ,      nan,      nan],
             [265.8125, 265.375 ,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan]],
    
            [[     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [266.9375, 266.6875, 267.375 ,      nan,      nan],
             [266.125 , 265.5625,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan],
             [     nan,      nan,      nan,      nan,      nan]]]],
          dtype=float32)

  • tp
    (Station, time, latitude, longitude)
    float32
    nan nan nan nan ... nan nan nan nan

    long_name :

    Total precipitation

    nameCDM :

    Total_precipitation_1hour_Accumulation

    nameECMWF :

    Total precipitation

    product_type :

    forecast

    shortNameECMWF :

    tp

    standard_name :

    precipitation_amount

    units :

    m

    array([[[[nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan]],
    
            [[nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan]],
    
            [[nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan]],
    ...
            [[nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [ 0.,  0.,  0., nan, nan],
             [ 0.,  0., nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan]],
    
            [[nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [ 0.,  0.,  0., nan, nan],
             [ 0.,  0., nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan]],
    
            [[nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [ 0.,  0.,  0., nan, nan],
             [ 0.,  0., nan, nan, nan],
             [nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan]]]], dtype=float32)

  • weights
    (Station, latitude, longitude)
    float64
    nan nan nan nan ... nan nan nan nan

    array([[[           nan,            nan,            nan,            nan,
                        nan],
            [           nan,            nan,            nan,            nan,
                        nan],
            [           nan,            nan,            nan,            nan,
                        nan],
            [           nan,            nan,            nan,            nan,
                        nan],
            [           nan,            nan,            nan, 7.44866197e-06,
             1.53143591e-04],
            [           nan,            nan,            nan, 9.07895661e-01,
             9.19437472e-02]],
    
           [[           nan, 1.04952703e-02,            nan,            nan,
                        nan],
            [           nan, 6.08058335e-01, 3.81446395e-01,            nan,
                        nan],
            [           nan,            nan,            nan,            nan,
                        nan],
            [           nan,            nan,            nan,            nan,
                        nan],
            [           nan,            nan,            nan,            nan,
                        nan],
            [           nan,            nan,            nan,            nan,
                        nan]],
    
           [[           nan,            nan,            nan,            nan,
                        nan],
            [           nan,            nan,            nan,            nan,
                        nan],
            [1.21056468e-01, 5.82377590e-01, 4.51483858e-02,            nan,
                        nan],
            [1.77790114e-02, 2.33638545e-01,            nan,            nan,
                        nan],
            [           nan,            nan,            nan,            nan,
                        nan],
            [           nan,            nan,            nan,            nan,
                        nan]]])

• Indexes: (4)
  • latitude
    PandasIndex

    PandasIndex(Float64Index([45.0, 45.25, 46.0, 46.25, 46.5, 46.75], dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Float64Index([-74.5, -74.25, -74.0, -71.0, -70.75], dtype='float64', name='longitude'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-01 00:00:00', '1979-01-01 01:00:00',
                   '1979-01-01 02:00:00', '1979-01-01 03:00:00',
                   '1979-01-01 04:00:00', '1979-01-01 05:00:00',
                   '1979-01-01 06:00:00', '1979-01-01 07:00:00',
                   '1979-01-01 08:00:00', '1979-01-01 09:00:00',
                   ...
                   '2020-12-31 14:00:00', '2020-12-31 15:00:00',
                   '2020-12-31 16:00:00', '2020-12-31 17:00:00',
                   '2020-12-31 18:00:00', '2020-12-31 19:00:00',
                   '2020-12-31 20:00:00', '2020-12-31 21:00:00',
                   '2020-12-31 22:00:00', '2020-12-31 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=368184, freq=None))

  • Station
    PandasIndex

    PandasIndex(Index(['023003', '031101', '040111'], dtype='object', name='Station'))

• Attributes: (3)

  institution :

  ECMWF

  source :

  Reanalysis

  title :

  ERA5 forecasts

station = '023003'

ds_station = xds.data.sel(Station=station)
ds_clipped = xds.bbox_clip(ds_station)
ds_clipped

<xarray.Dataset>
Dimensions:     (time: 368184, latitude: 2, longitude: 2)
Coordinates:
  * latitude    (latitude) float32 46.5 46.75
  * longitude   (longitude) float32 -71.0 -70.75
  * time        (time) datetime64[ns] 1979-01-01 ... 2020-12-31T23:00:00
    Station     <U6 '023003'
    Superficie  object '208.4591919813271'
Data variables:
    t2m         (time, latitude, longitude) float32 270.8 270.3 ... 269.3 268.8
    tp          (time, latitude, longitude) float32 nan nan nan ... 0.0 0.0 0.0
    weights     (latitude, longitude) float64 7.449e-06 0.0001531 0.9079 0.09194
Attributes:
    institution:  ECMWF
    source:       Reanalysis
    title:        ERA5 forecasts

xarray.Dataset

• Dimensions:
  • time: 368184
  • latitude: 2
  • longitude: 2
• Coordinates: (5)
  • latitude
    (latitude)
    float32
    46.5 46.75

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([46.5 , 46.75], dtype=float32)

  • longitude
    (longitude)
    float32
    -71.0 -70.75

    array([-71.  , -70.75], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1979-01-01 ... 2020-12-31T23:00:00

    standard_name :

    time

    array(['1979-01-01T00:00:00.000000000', '1979-01-01T01:00:00.000000000',
           '1979-01-01T02:00:00.000000000', ..., '2020-12-31T21:00:00.000000000',
           '2020-12-31T22:00:00.000000000', '2020-12-31T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • Station
    ()
    <U6
    '023003'

    array('023003', dtype='<U6')

  • Superficie
    ()
    object
    '208.4591919813271'

    array('208.4591919813271', dtype=object)

• Data variables: (3)
  • t2m
    (time, latitude, longitude)
    float32
    270.8 270.3 268.8 ... 269.3 268.8

    long_name :

    2 metre temperature

    nameCDM :

    2_metre_temperature_surface

    nameECMWF :

    2 metre temperature

    product_type :

    analysis

    shortNameECMWF :

    2t

    standard_name :

    air_temperature

    units :

    K

    array([[[270.8125, 270.3125],
            [268.8125, 268.5625]],
    
           [[270.9375, 270.5   ],
            [269.0625, 268.9375]],
    
           [[271.25  , 270.75  ],
            [269.3125, 269.25  ]],
    
           ...,
    
           [[269.75  , 269.0625],
            [270.4375, 269.875 ]],
    
           [[268.0625, 267.375 ],
            [269.    , 268.5   ]],
    
           [[268.375 , 267.75  ],
            [269.3125, 268.75  ]]], dtype=float32)

  • tp
    (time, latitude, longitude)
    float32
    nan nan nan nan ... 0.0 0.0 0.0 0.0

    long_name :

    Total precipitation

    nameCDM :

    Total_precipitation_1hour_Accumulation

    nameECMWF :

    Total precipitation

    product_type :

    forecast

    shortNameECMWF :

    tp

    standard_name :

    precipitation_amount

    units :

    m

    array([[[nan, nan],
            [nan, nan]],
    
           [[nan, nan],
            [nan, nan]],
    
           [[nan, nan],
            [nan, nan]],
    
           ...,
    
           [[ 0.,  0.],
            [ 0.,  0.]],
    
           [[ 0.,  0.],
            [ 0.,  0.]],
    
           [[ 0.,  0.],
            [ 0.,  0.]]], dtype=float32)

  • weights
    (latitude, longitude)
    float64
    7.449e-06 0.0001531 0.9079 0.09194

    array([[7.44866197e-06, 1.53143591e-04],
           [9.07895661e-01, 9.19437472e-02]])

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Float64Index([46.5, 46.75], dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Float64Index([-71.0, -70.75], dtype='float64', name='longitude'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-01 00:00:00', '1979-01-01 01:00:00',
                   '1979-01-01 02:00:00', '1979-01-01 03:00:00',
                   '1979-01-01 04:00:00', '1979-01-01 05:00:00',
                   '1979-01-01 06:00:00', '1979-01-01 07:00:00',
                   '1979-01-01 08:00:00', '1979-01-01 09:00:00',
                   ...
                   '2020-12-31 14:00:00', '2020-12-31 15:00:00',
                   '2020-12-31 16:00:00', '2020-12-31 17:00:00',
                   '2020-12-31 18:00:00', '2020-12-31 19:00:00',
                   '2020-12-31 20:00:00', '2020-12-31 21:00:00',
                   '2020-12-31 22:00:00', '2020-12-31 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=368184, freq=None))

• Attributes: (3)

  institution :

  ECMWF

  source :

  Reanalysis

  title :

  ERA5 forecasts

((ds_clipped \
.t2m \
.isel(time=0) \
.hvplot(tiles='ESRI',
        geo=True,
        width=750,
        height=450) * \
gdf[gdf.Station == station] \
.hvplot(geo=True,
           color='Station',
           alpha=0.8,
           width=750,
           height=450,
           legend='top',
           hover_cols=['Station','Superficie'])) + \
ds_clipped \
.weights \
.hvplot(tiles='ESRI',
        geo=True,
        width=750,
        height=450) * \
gdf[gdf.Station == station] \
.hvplot(geo=True,
           color='Station',
           alpha=0.8,
           width=750,
           height=450,
           legend='top',
           hover_cols=['Station','Superficie'])).cols(1)

%%time

# http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases

query = {
    "variables": {"era5_reanalysis_single_levels": ["t2m", "tp"]},
    "space": {
        "clip": "polygon", # bbox, point or polygon
        "aggregation": True,
        "geometry": gdf,
        "unique_id": "Station"
    },
    "time": {
        "timestep": "D",
        "aggregation": {"tp": [np.nansum],
                        "t2m": [np.nanmax, np.nanmin]},

        "start": '1979-01-01',
        "end": '2020-12-31',
        "timezone": 'America/Montreal',
    },
}


xds = xd.Dataset(**query)

3it [00:02,  1.07it/s]
Processing tp: 100%|██████████| 2/2 [00:07<00:00,  3.62s/it]

CPU times: user 14.3 s, sys: 892 ms, total: 15.2 s
Wall time: 19.5 s

xds.data

<xarray.Dataset>
Dimensions:     (Station: 3, time: 15342)
Coordinates:
    longitude   (Station) float64 -70.94 -74.14 -74.27
    latitude    (Station) float64 46.72 45.18 46.08
  * Station     (Station) object '023003' '031101' '040111'
    Superficie  (Station) object '208.4591919813271' ... '433.440893903503'
  * time        (time) datetime64[ns] 1978-12-31 1979-01-01 ... 2020-12-31
Data variables:
    t2m_nanmax  (time, Station) float32 271.1 276.4 271.5 ... 273.7 276.1 272.7
    t2m_nanmin  (time, Station) float32 268.8 274.4 269.7 ... 269.0 271.3 266.3
    tp_nansum   (time, Station) float32 0.0 0.0 0.0 ... 0.002386 0.002362
Attributes:
    institution:  ECMWF
    source:       Reanalysis
    title:        ERA5 forecasts
    timezone:     America/Montreal

xarray.Dataset

• Dimensions:
  • Station: 3
  • time: 15342
• Coordinates: (5)
  • longitude
    (Station)
    float64
    -70.94 -74.14 -74.27

    array([-70.94448103, -74.14353175, -74.27460773])

  • latitude
    (Station)
    float64
    46.72 45.18 46.08

    array([46.71517431, 45.17637767, 46.08306936])

  • Station
    (Station)
    object
    '023003' '031101' '040111'

    array(['023003', '031101', '040111'], dtype=object)

  • Superficie
    (Station)
    object
    '208.4591919813271' ... '433.440...

    array(['208.4591919813271', '111.7131058782722', '433.440893903503'],
          dtype=object)

  • time
    (time)
    datetime64[ns]
    1978-12-31 ... 2020-12-31

    array(['1978-12-31T00:00:00.000000000', '1979-01-01T00:00:00.000000000',
           '1979-01-02T00:00:00.000000000', ..., '2020-12-29T00:00:00.000000000',
           '2020-12-30T00:00:00.000000000', '2020-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (3)
  • t2m_nanmax
    (time, Station)
    float32
    271.1 276.4 271.5 ... 276.1 272.7

    long_name :

    2 metre temperature

    nameCDM :

    2_metre_temperature_surface

    nameECMWF :

    2 metre temperature

    product_type :

    analysis

    shortNameECMWF :

    2t

    standard_name :

    air_temperature

    units :

    K

    array([[271.1253 , 276.40222, 271.52487],
           [278.8807 , 281.0484 , 277.00375],
           [277.17114, 276.26514, 273.84592],
           ...,
           [272.68164, 270.683  , 264.64752],
           [269.04025, 275.22174, 270.5143 ],
           [273.73267, 276.10953, 272.7495 ]], dtype=float32)

  • t2m_nanmin
    (time, Station)
    float32
    268.8 274.4 269.7 ... 271.3 266.3

    long_name :

    2 metre temperature

    nameCDM :

    2_metre_temperature_surface

    nameECMWF :

    2 metre temperature

    product_type :

    analysis

    shortNameECMWF :

    2t

    standard_name :

    air_temperature

    units :

    K

    array([[268.78976, 274.38742, 269.73624],
           [271.37527, 274.3489 , 271.82175],
           [269.25568, 263.26852, 260.843  ],
           ...,
           [263.42   , 265.07678, 257.20084],
           [260.9095 , 264.93744, 258.1872 ],
           [268.95377, 271.25836, 266.3292 ]], dtype=float32)

  • tp_nansum
    (time, Station)
    float32
    0.0 0.0 0.0 ... 0.002386 0.002362

    long_name :

    Total precipitation

    nameCDM :

    Total_precipitation_1hour_Accumulation

    nameECMWF :

    Total precipitation

    product_type :

    forecast

    shortNameECMWF :

    tp

    standard_name :

    precipitation_amount

    units :

    m

    array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
           [1.63621679e-02, 1.32727744e-02, 2.14345567e-02],
           [1.62692089e-02, 5.28216781e-03, 3.99874523e-03],
           ...,
           [7.54918088e-04, 1.28116096e-06, 0.00000000e+00],
           [5.90828713e-03, 6.06924109e-03, 1.17766075e-02],
           [4.65553068e-03, 2.38613598e-03, 2.36194278e-03]], dtype=float32)

• Indexes: (2)
  • Station
    PandasIndex

    PandasIndex(Index(['023003', '031101', '040111'], dtype='object', name='Station'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1978-12-31', '1979-01-01', '1979-01-02', '1979-01-03',
                   '1979-01-04', '1979-01-05', '1979-01-06', '1979-01-07',
                   '1979-01-08', '1979-01-09',
                   ...
                   '2020-12-22', '2020-12-23', '2020-12-24', '2020-12-25',
                   '2020-12-26', '2020-12-27', '2020-12-28', '2020-12-29',
                   '2020-12-30', '2020-12-31'],
                  dtype='datetime64[ns]', name='time', length=15342, freq='D'))

• Attributes: (4)

  institution :

  ECMWF

  source :

  Reanalysis

  title :

  ERA5 forecasts

  timezone :

  America/Montreal

xds.data.to_dataframe().hvplot(x='time',
                               y=['t2m_nanmax','t2m_nanmin'],
                               grid=True,
                               width=750,
                               height=450,
                               groupby='Station',
                               legend='top',
                               widget_location='bottom')

xds.data.to_dataframe().hvplot(x='time',
                               y=['tp_nansum'],
                               grid=True,
                               width=750,
                               height=450,
                               groupby='Station',
                               legend='top',
                               widget_location='bottom')